Data extraction: What it is and how it works

types of predictive modeling

Data extraction is the process of accessing, collecting & importing data. Discover some examples of data extraction tools & how they work here.

Data visualization created with data extraction.
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Most businesses have access to more data than ever before. And the majority of these organizations have no problem collecting data; however, several businesses face the challenge of putting this data to good use and deriving valuable insights from it.

SEE: Big data policy (TechRepublic Premium)

To meet the challenge of making growing datasets more relevant and useful, businesses need to integrate their datasets across different sources. But before they can do that, they need to extract the data. In this article, we cover what data extraction is, how it works and the top data extraction tools to consider for your business.

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What is data extraction?

Data extraction is the process of gathering unstructured data from disparate sources and storing it in a manner that makes it easily accessible. It typically involves processing data from unstructured sources to transform it into a more organized and accessible format.

SEE: 5 tips to improve data quality for unstructured data (TechRepublic)

Sources for data extraction can include spreadsheets, SaaS platforms, emails and invoices. Extracted data is stored in a centralized location on the cloud, on-site or in a hybrid environment.

How does data extraction work?

Data extraction can be a manual or automated process, depending on if you incorporate data extraction tools. Regardless of how hands-on your data team plans to be, there are three core steps that make data extraction possible:

  • Analyze the format of source data: This helps you to check and prepare for data structure changes, including adding new rows, columns or tables.
  • Retrieve data based on the data integration replication scheme: This step will involve collecting and organizing data into the target fields and tables. This step also involves selecting part of the data to be extracted.
  • Perform the extraction to load into a specific destination: Destination options include a cloud server, data warehouse or other targets.

What are the types of data extraction?

Full extraction

In this type of data extraction, the entire data source is extracted as-is and then exported. There is no need to select parts of the data or perform any checks at the time of extraction; this is a complete download of data in its current state.

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Full extraction is best for when you don’t need to check dataset changes that have happened since your last extraction and/or when you need complete access to all of your data. However, it’s important to know that workload resources and lag times can get particularly high when you need to do a full extraction of a larger dataset.

Incremental extraction

In incremental extraction, the part of the data that needs to be extracted is selected, and changes to the data are tracked. Because data is selected and transformed in each stage of incremental extraction, it is a much more involved process than full extraction.

SEE: Best data preparation software (TechRepublic)

Incremental extraction requires more complex logic compared to full extraction. However, system workloads are significantly reduced, as the volume of data that is extracted is typically smaller. In most cases, incremental extraction is a more efficient process, giving the next stage of data pipelines a more manageable volume of data to process.

Update notification

With the update notification approach to data extraction, data is extracted every time someone updates the dataset. You can schedule automatic extraction or pull data manually when data changes occur. Update notification for data extraction helps gather and update data regularly but requires the extraction to be completed each time any part of the data is updated.

Data extraction and ETL

Data extraction is the first step in the extract, transform and load process, which is a component of data integration strategy that prepares data for analysis. The overall goal of ETL is to allow organizations to gather data from different sources into a single location.

SEE: Job description: ETL/data warehouse developer (TechRepublic Premium)

Data extraction plays a key role in making ETL possible. Once data extraction is complete, data cleansing and other data transformation methods are applied to ensure it is compatible with the next data destination. In the last step of ETL, data is loaded into a central repository for analysis.

Data extraction tools are designed to make it easier and faster to extract data. Here are some of the top data extraction tools for varying business use cases.

Import.io

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Image: Import.io

Import.io extracts data from websites, social media, databases and other sources. It is easy to use, making it ideal for users of all skill levels. There is no need to write any code to use this application. Key features include IP address extraction, email extraction and pricing extraction. It also offers reporting and data visualization features.

ScrapeStorm

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Image: ScrapeStorm

ScrapeStorm is an artificial intelligence-powered data extraction tool that can be used to automatically detect the type of data to be extracted, such as numbers, images or prices. The user interface is simple and intuitive. Users can choose from various export strategies and target sources, including MySQL, CSV, TXT and WordPress.

Nanonets

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Image: Nanonets

Nanonets is a popular data extraction tool that leverages machine learning and AI capabilities to automate extraction processes. It can be used to extract data from webpages, emails and documents and load them into customer relationship management solutions, accounting software, enterprise resource planning tools, databases and other applications. Key features of Nanonets include workflow management, online character recognition, a web scraper and an email parser.

Read next: Best ETL tools and software (TechRepublic)